Relevance of Higher-Order Epistasis in Drug Resistance

Abstract

We studied five chemically distinct but related 1,3,5-triazine antifolates with regard to their effects on growth of a set of mutants in dihydrofolate reductase (DHFR). The mutants comprise a combinatorially complete data set of all 16 possible combinations of 4 amino acid replacements associated with resistance to pyrimethamine in the malaria parasite Plasmodium falciparum. Pyrimethamine was a mainstay medication for malaria for many years, and it is still in use in intermittent treatment during pregnancy or as a partner drug in artemisinin combination therapy. Our goal was to investigate the extent to which the alleles yield similar adaptive topographies and patterns of epistasis across chemically related drugs. We find that the adaptive topographies are indeed similar with the same or closely related alleles being fixed in computer simulations of stepwise evolution. For all but one of the drugs the topography features at least one suboptimal fitness peak. Our data are consistent with earlier results indicating that third order and higher epistatic interactions appear to contribute only modestly to the overall adaptive topography, and they are largely conserved. In regard to drug development, our data suggest that higher-order interactions are likely to be of little value as an advisory tool in the choice of lead compounds.

Higher-order epistasis creates idiosyncrasy, confounding predictions in protein evolution

“…For some studies, the mutations were accumulated through directed evolution or protein engineering of a novel function (phosphotriesterase, PTE; β-lactamase, OXA-48; nitroreductase, NfsA) 13,[22][23][24] . For the remaining studies, the positions were identified from naturally occurring evolutionary trajectories, either through a retrospectively identified path using ancestral sequence reconstruction (methyl-parathion hydrolase, MPH) 14,25 , the presence of clinically relevant mutations (dihydrofolate reductase, DHFR and β-lactamase, TEM-1) 11,[26][27][28] , or in the case of alkaline phosphatase (AP), by using previously characterised active site residue mutations 29 . The final dataset consisted of 56 unique mutations, of which 54 are located within the protein open reading frame, and two are located in a promoter region (in DHFR and TEM-1) 11,23 .…”

“…28 explored 15 inhibitors for the same set of four mutations c Two separate mutational trajectories for the same protein and substrate d Ref. 26 explored four mutations using five different substrates; ref. 27 explored both kcat/KM and KI for two mutational trajectories e Ref.…”

The mutation effect reaction norm (mu‐rn) highlights environmentally dependent mutation effects and epistatic interactions

“…2013, 2018; Lozovsky et al. 2020). “Order” corresponds to the number of actors involved in an interaction.…”

“…2019; Lozovsky et al. 2020), there have been few formal attempts to integrate details of the environment into measurements of mutation effects and interactions. In this study, I introduce the “mutation effect reaction norm,” an abstraction that combines the reaction norm with mutation effects and physiological epistasis.…”

Higher order genetic interactions switch cancer genes from two-hit to one-hit drivers

“…In the simplest examples—third-order interactions—the interaction between two mutations changes depending upon whether a third mutation is present or not. Third-order interactions can occur among mutations in the same 19 , 26 , 27 or different 17 , 28 genes and make important contributions to diverse phenotypic traits such as growth and drug resistance 18 , 29 . We hypothesized that conceptually similar higher-order interactions might be occurring in cancer genomes, with mutations in a second gene (between gene interaction) altering the interaction between a mutation and CNA in a cancer gene (within gene interaction) (Fig.…”